The tragic events of the Indian Ocean tsunami on 26 December 2004 highlighted the need for reliable and effective alert and response sysems for tsunami threat to Australian communities. Geoscience Australia has established collaborative partnerships with state and federal emergency management agencies to support better preparedness and to improve community awareness of tsunami risks.

This folder contains WindRiskTech data used in preliminary stages of the National Wind Risk Assessment. The data are synthetic TC event sets, generated by a statistical-dynamical model of TCs that can be applied to general circulation models to provide projections of TC activity. Output from two GCMs is available here - the NCAR CCSM3 and the GFDL CM2.1 model. For each, there are a number of scenarios (based on the SRES scenarios from AR4 and previous IPCC reports) and time periods (the time periods are not the same for the A1B scenario). For each mode, scenario and time period, the data are a set of 1000 TC track files in tab-delimited format contained in the huur.zip files in each sub-folder. The output folder contains the output of running TCRM (pre-2011 version) on each of the datasets.

The major tsunamis of the last few years in the southern hemisphere have raised awareness of the possibility of potentially damaging tsunami to Australia and countries in the Southwest Pacific region. Here we present a probabilistic hazard assessment for Australia and for the SOPAC countries in the Southwest Pacific for tsunami generated by subduction zone earthquakes. To conduct a probabilistic tsunami hazard assessment, we first need to estimate the likelihood of a tsunamigeneic earthquake occurring. Here we will discuss and present our method of estimate the likely return period a major megathrust earthquake on each of the subduction zones surrounding the Pacific. Our method is based on the global rate of occurrence of such events and the rate of convergence and geometry of each particular subduction zone. This allows us to create a synthetic catalogue of possible megathrust earthquakes in the region with associated probabilities for each event. To calculate the resulting tsunami for each event we create a library of "unit source" tsunami for a set of 100km x 50km unit sources along each subduction zone. For each unit source, we calculate the sea floor deformation by modelling the slip along the unit source as a dislocation in a stratified, linear elastic half-space. This sea floor deformation is then fed into a tsunami propagation model to calculate the wave height off the coast for each unit source. Our propagation model uses a staggered grid, finite different scheme to solve the linear, shallow water wave equations for tsunami propagation. The tsunami from any earthquake in the synthetic catalogue can then be quickly calculated by summing the unit source tsunami from all the unit sources that fall within the rupture zone of the earthquake. The results of these calculations can then be combined with our estimate of the probability of the earthquake to produce hazard maps showing (for example) the probability of a tsunami exceeding a given height offshore from a given stretch of coastline. These hazard maps can then be used to guide emergency managers to focus their planning efforts on regions and countries which have the greatest likelihood of producing a catastrophic tsunami.

Random forest (RF) is one of the top performed methods in predictive modelling. Because of its high predictive accuracy, we introduced it into spatial statistics by combining it with the existing spatial interpolation methods, resulting a few hybrid methods and improved prediction accuracy when applied to marine environmental datasets (Li et al., 2011). The superior performance of these hybrid methods was partially attributed to the features of RF, one component of the hybrids. One of these features inherited from its trees is to be able to deal with irrelevant inputs. It is also argued that the performance of RF is not much influenced by parameter choices, so the hybrids presumably also share this feature. However, these assumptions have not been tested for the spatial interpolation of environmental variables. In this study, we experimentally examined these assumptions using seabed sand and gravel content datasets on the northwest Australian marine margin. Four sets of input variables and two choices of 'number of variables randomly sampled as candidates at each split' were tested in terms of predictive accuracy. The input variables vary from six predictors only to combinations of these predictors and derived variables including the second and third orders and/or possible two-way interactions of these six predictors. However, these derived predictors were regarded as redundant and irrelevant variables because they are correlated with these six predictors and because RF can do implicit variable selection and can model complex interactions among predictors. The results derived from this experiment are analysed, discussed and compared with previous findings. The outcomes of this study have both practical and theoretical importance for predicting environmental variables.

The Attorney General's Departement has supported Geoscience Australia to develop inundation models for four east coast communities with the view of buildling the tsunami planning and preparation capacity of the Jurisdictions. The aim of this document and accompanying DVD is to report on the approach adopted by each Jurisdiction, the modelling outcomes and supply the underpinning computer scripts and input data.

Population connectivity research involves investigating the presence, strength and characteristics of spatial and temporal relationships between populations. These data can be used in many different ways: to identify source-sink relationships between populations; to detect critical pathways or keystone habitats; to find natural clusters or biogeographic regions; or to investigate the processes underlying population genetic structure, among others. This information can be of significant value for managers and decision-makers when designing reserve networks, evaluating the potential spread of invasive species. This database represents the first publicly-available collection of national/continental-scale marine connectivity data.

The aim of this project is to equip ANUGA with a storm surge capability in partnership with the Department of Planning Western Australia (DoP), take steps to validate the methodology and provide a case study to DoP in the form of a storm surge scenario for Bunbury. The developed capability will provide a mechanism whereby DoP can investigate mitigation options for a range of hydrodynamic hazards.

Following the tragic events of the Indian Ocean tsunami on 26 December 2004 it became obvious there were shortcomings in the response and alert systems for the threat of tsunami to Western Australia's (WA) coastal communities. The relative risk of a tsunami event to the towns, remote indigenous communities, and infrastructure for the oil, gas and mining industries was not clearly understood in 2004. Consequently, no current detailed response plans for a tsunami event in WA coastal areas existed. The Boxing Day event affected the WA coastline from Bremer Bay on the south coast, to areas north of Exmouth on the north-west coast, with a number of people requiring rescue from abnormally strong currents and rips. There were also reports of personal belongings at some beaches inundated by wave activity. More than 30 cm of water flowed down a coast-side road in Geraldton on the mid-west coast, and Geordie Bay at Rottnest Island (19 km of the coast of Fremantle) experienced five 'tides' in three hours, resulting in boats hitting the ocean bed a number of times. The vivid images of the devastation caused by the 2004 event across a wide geographical area changed the perception of tsunami and achieved an appreciation of the potential enormity of impact from this low frequency but high consequence natural hazard. With WA's proximity to the Sunda Arc, which is widely recognised as a high probability area for intra-plate earthquakes, the need to develop a better understanding of tsunami risk and model the potential social and economic impacts on communities and critical infrastructure along the Western Australian coast, became a high priority. Under WA's emergency management arrangements, the Fire and Emergency Services Authority (FESA) has responsibility for ensuring effective emergency management is in place for tsunami events across the PPRR framework.

In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content for the AEEZ using samples extracted on August 2010 from Geoscience Australia's Marine Samples Database. The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region but the choice of mtry. No single method performs best for all the tested scenarios. Of the 18 compared methods, RFIDS and RFOK are the most accurate methods in all three regions. Overall, of the 36 combinations of input secondary variables, methods and regions, RFIDS, 6RFIDS and RFOK were among the most accurate methods in all three regions. Model averaging further improved the prediction accuracy. The most accurate methods reduced the prediction error by up to 7%. RFOKRFIDS, with a search window size of 5, an mtry of 4 and more realistic predictions in comparison with the control, is recommended for predicting sand content across the AEEZ if a single method is required. This study provides suggestions and guidelines for improving the spatial interpolations of marine environmental data.

Abstract for the final talk in the earthquake hazard map session planned for the 2011 AEES meeting.